Paediatric pharmacovigilance: use of pharmacovigilance data mining algorithms for signal detection in a safety dataset of a paediatric clinical study conducted in seven African countries

BACKGROUND: Pharmacovigilance programmes monitor and help ensuring the safe use of medicines which is critical to the success of public health programmes. The commonest method used for discovering previously unknown safety risks is spontaneous notifications. In this study we examine the use of data mining algorithms to identify signals from adverse events reported in a phase IIIb/IV clinical trial evaluating the efficacy and safety of several Artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria in African children. METHODS: We used paediatric safety data from a multi-site, multi-country clinical study conducted in seven African countries (Burkina Faso, Gabon, Nigeria, Rwanda, Uganda, Zambia, and Mozambique). Each site compared three out of four ACTs, namely amodiaquine-artesunate (ASAQ), dihydroartemisinin-piperaquine (DHAPQ), artemether-lumefantrine (AL) or chlorproguanil/dapsone and artesunate (CD+A). We examine two pharmacovigilance signal detection methods, namely proportional reporting ratio and Bayesian Confidence Propagation Neural Network on the clinical safety dataset. RESULTS: Among the 4,116 children (6-59 months old) enrolled and followed up for 28 days post treatment, a total of 6,238 adverse events were reported resulting into 346 drug-event combinations. Nine signals were generated both by proportional reporting ratio and Bayesian Confidence Propagation Neural Network. A review of the manufacturer package leaflets, an online Multi-Drug Symptom/Interaction Checker (DoubleCheckMD) and further by therapeutic area experts reduced the number of signals to five. The ranking of some drug-adverse reaction pairs on the basis of their signal index differed between the two methods. CONCLUSIONS: Our two data mining methods were equally able to generate suspected signals using the pooled safety data from a phase IIIb/IV clinical trial. This analysis demonstrated the possibility of utilising clinical studies safety data for key pharmacovigilance activities like signal detection and evaluation. This approach can be applied to complement the spontaneous reporting systems which are limited by under reporting

Genome sequencing and the diagnosis of novel coronavirus (SARS-COV-2) in Africa: how far are we?

The coronavirus disease (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) has become a pandemic. There is currently no vaccine or effective treatment for COVID-19. Early diagnosis and management is key to favourable outcomes. In order to prevent more widespread transmission of the virus, rapid detection and isolation of confirmed cases is of utmost importance. Real time reverse transcriptase polymerase chain reaction (RT-PCR) is currently the "gold standard" for the detection of SARS-COV-2. There are several challenges associated with this test from sample collection to processing and the longer turnaround time for the results to be available. More rapid and faster diagnostic tests that may produce results within minutes to a few hours will be instrumental in controlling the disease. Serological tests that detect specific antibodies to the virus may be such options. In this review, we extensively searched for studies that compared RT-PCR with serological tests for the diagnosis of COVID-19. We extracted the data from the various selected studies that compared the different tests and summarised the available evidence to determine which test is more appropriate especially in Africa. We also reviewed the current evidence and the challenges for the genome sequencing of SARS-COV-2 in Africa. Finally, we discuss the relevance of the different diagnostic tests and the importance of genome sequencing in identifying potential therapeutic options for the control of COVID-19 in Africa

Spatial and temporal distribution of infectious disease epidemics, disasters and other potential public health emergencies in the World Health Organisation Africa region, 2016-2018

Background Emerging and re-emerging diseases with pandemic potential continue to challenge fragile health systems in Africa, creating enormous human and economic toll. To provide evidence for the investment case for public health emergency preparedness, we analysed the spatial and temporal distribution of epidemics, disasters and other potential public health emergencies in the WHO African region between 2016 and 2018. Methods We abstracted data from several sources, including: the WHO African Region’s weekly bulletins on epidemics and emergencies, the WHO-Disease Outbreak News (DON) and the Emergency Events Database (EM-DAT) of the Centre for Research on the Epidemiology of Disasters (CRED). Other sources were: the Program for Monitoring Emerging Diseases (ProMED) and the Global Infectious Disease and Epidemiology Network (GIDEON). We included information on the time and location of the event, the number of cases and deaths and counter-checked the different data sources. Data analysis We used bubble plots for temporal analysis and generated graphs and maps showing the frequency and distribution of each event. Based on the frequency of events, we categorised countries into three: Tier 1, 10 or more events, Tier 2, 5–9 events, and Tier 3, less than 5 or no event. Finally, we compared the event frequencies to a summary International Health Regulations (IHR) index generated from the IHR technical area scores of the 2018 annual reports. Results Over 260 events were identified between 2016 and 2018. Forty-one countries (87%) had at least one epidemic between 2016 and 2018, and 21 of them (45%) had at least one epidemic annually. Twenty-two countries (47%) had disasters/humanitarian crises. Seven countries (the epicentres) experienced over 10 events and all of them had limited or developing IHR capacities. The top five causes of epidemics were: Cholera, Measles, Viral Haemorrhagic Diseases, Malaria and Meningitis. Conclusions The frequent and widespread occurrence of epidemics and disasters in Africa is a clarion call for investing in preparedness. While strengthening preparedness should be guided by global frameworks, it is the responsibility of each government to finance country specific needs. We call upon all African countries to establish governance and predictable financing mechanisms for IHR implementation and to build resilient health systems everywhere

Ranking of drug-event combinations according to the signal index of the PRR (Upper panel) and BCPNN (Lower panel) methods.

<p>Ranking of drug-event combinations according to the signal index of the PRR (Upper panel) and BCPNN (Lower panel) methods.</p

A two by two table for the adverse event-drug pair.

<p>*AE = Adverse Event; <i>a = </i>the number of reports involving the drug of interest <i>j</i> and adverse event of interest <i>i</i> combination; <i>b = </i>reports of adverse event of interest <i>i</i> observed with other drugs; <i>c = </i>reports of all other AEs with drug <i>j; d = </i>reports of all other AEs with the other drugs; and <i>a+b+c+d</i> = the total number of reports in the dataset.</p

A Comparison of suspected signals detected by the two data mining algorithms.

<p>AL = Artemether-Lumefantrine; ASAQ = Artesunate-Amodiaquine; DHAPQ = Dihydroartemisinin- Piperaquine; WBC = White Blood Cells; ALT = Alanine amino Transferase.</p

Paediatric pharmacovigilance: use of pharmacovigilance data mining algorithms for signal detection in a safety dataset of a paediatric clinical study conducted in seven african countries

Pharmacovigilance programmes monitor and help ensuring the safe use of medicines which is critical to the success of public health programmes. The commonest method used for discovering previously unknown safety risks is spontaneous notifications. In this study we examine the use of data mining algorithms to identify signals from adverse events reported in a phase IIIb/IV clinical trial evaluating the efficacy and safety of several Artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria in African children

Sustainable strategies for Ebola virus disease outbreak preparedness in Africa: a case study on lessons learnt in countries neighbouring the Democratic Republic of the Congo

Abstract Background From May 2018 to September 2022, the Democratic Republic of Congo (DRC) experienced seven Ebola virus disease (EVD) outbreaks within its borders. During the 10th EVD outbreak (2018–2020), the largest experienced in the DRC and the second largest and most prolonged EVD outbreak recorded globally, a WHO risk assessment identified nine countries bordering the DRC as moderate to high risk from cross border importation. These countries implemented varying levels of Ebola virus disease preparedness interventions. This case study highlights the gains and shortfalls with the Ebola virus disease preparedness interventions within the various contexts of these countries against the background of a renewed and growing commitment for global epidemic preparedness highlighted during recent World Health Assembly events. Main text Several positive impacts from preparedness support to countries bordering the affected provinces in the DRC were identified, including development of sustained capacities which were leveraged upon to respond to the subsequent coronavirus disease 2019 (COVID-19) pandemic. Shortfalls such as lost opportunities for operationalizing cross-border regional preparedness collaboration and better integration of multidisciplinary perspectives, vertical approaches to response pillars such as surveillance, over dependence on external support and duplication of efforts especially in areas of capacity building were also identified. A recurrent theme that emerged from this case study is the propensity towards implementing short-term interventions during active Ebola virus disease outbreaks for preparedness rather than sustainable investment into strengthening systems for improved health security in alignment with IHR obligations, the Sustainable Development Goals and advocating global policy for addressing the larger structural determinants underscoring these outbreaks. Conclusions Despite several international frameworks established at the global level for emergency preparedness, a shortfall exists between global policy and practice in countries at high risk of cross border transmission from persistent Ebola virus disease outbreaks in the Democratic Republic of Congo. With renewed global health commitment for country emergency preparedness resulting from the COVID-19 pandemic and cumulating in a resolution for a pandemic preparedness treaty, the time to review and address these gaps and provide recommendations for more sustainable and integrative approaches to emergency preparedness towards achieving global health security is now

Temporal distribution of Plasmodium falciparum recrudescence following artemisinin-based combination therapy : an individual participant data meta-analysis

Background: The duration of trial follow-up affects the ability to detect recrudescent infections following anti-malarial treatment. The aim of this study was to explore the proportions of recrudescent parasitaemia as ascribed by genotyping captured at various follow-up time-points in treatment efficacy trials for uncomplicated Plasmodium falciparum malaria. Methods: Individual patient data from 83 anti-malarial efficacy studies collated in the WorldWide Antimalarial Resistance Network (WWARN) repository with at least 28 days follow-up were available. The temporal and cumulative distributions of recrudescence were characterized using a Cox regression model with shared frailty on study-sites. Fractional polynomials were used to capture non-linear instantaneous hazard. The area under the density curve (AUC) of the constructed distribution was used to estimate the optimal follow-up period for capturing a P. falciparum malaria recrudescence. Simulation studies were conducted based on the constructed distributions to quantify the absolute overestimation in efficacy due to sub-optimal follow-up. Results: Overall, 3703 recurrent infections were detected in 60 studies conducted in Africa (15,512 children aged &lt; 5 years) and 23 studies conducted in Asia and South America (5272 patients of all ages). Using molecular genotyping, 519 (14.0%) recurrences were ascribed as recrudescent infections. A 28 day artemether-lumefantrine (AL) efficacy trial would not have detected 58% [95% confidence interval (CI) 47-74%] of recrudescences in African children and 32% [95% CI 15-45%] in patients of all ages in Asia/South America. The corresponding estimate following a 42 day dihydroartemisinin-piperaquine (DP) efficacy trial in Africa was 47% [95% CI 19-90%] in children under 5 years old treated with &gt; 48 mg/kg total piperaquine (PIP) dose and 9% [95% CI 0-22%] in those treated with &lt;= 48 mg/kg PIP dose. In absolute terms, the simulation study found that trials limited to 28 days follow-up following AL underestimated the risk of recrudescence by a median of 2.8 percentage points compared to day 63 estimates and those limited to 42 days following DP underestimated the risk of recrudescence by a median of 2.0 percentage points compared to day 42 estimates. The analysis was limited by few clinical trials following patients for longer than 42 days (9 out of 83 trials) and the imprecision of PCR genotyping which overcalls recrudescence in areas of higher transmission biasing the later distribution. Conclusions: Restricting follow-up of clinical efficacy trials to day 28 for AL and day 42 for DP will miss a proportion of late recrudescent treatment failures but will have a modest impact in derived efficacy. The results highlight that as genotyping methods improve consideration should be given for trials with longer duration of follow-up to detect early indications of emerging drug resistance

Competing risk events in antimalarial drug trials in uncomplicated Plasmodium falciparum malaria: A WorldWide Antimalarial Resistance Network individual participant data meta-analysis

Background: Therapeutic efficacy studies in uncomplicated Plasmodium falciparum malaria are confounded by new infections, which constitute competing risk events since they can potentially preclude/pre-empt the detection of subsequent recrudescence of persistent, sub-microscopic primary infections. Methods: Antimalarial studies typically report the risk of recrudescence derived using the Kaplan-Meier (K-M) method, which considers new infections acquired during the follow-up period as censored. Cumulative Incidence Function (CIF) provides an alternative approach for handling new infections, which accounts for them as a competing risk event. The complement of the estimate derived using the K-M method (1 minus K-M), and the CIF were used to derive the risk of recrudescence at the end of the follow-up period using data from studies collated in the WorldWide Antimalarial Resistance Network data repository. Absolute differences in the failure estimates derived using these two methods were quantified. In comparative studies, the equality of two K-M curves was assessed using the log-rank test, and the equality of CIFs using Gray's k-sample test (both at 5% level of significance). Two different regression modelling strategies for recrudescence were considered: cause-specific Cox model and Fine and Gray's sub-distributional hazard model. Results: Data were available from 92 studies (233 treatment arms, 31,379 patients) conducted between 1996 and 2014. At the end of follow-up, the median absolute overestimation in the estimated risk of cumulative recrudescence by using 1 minus K-M approach was 0.04% (interquartile range (IQR): 0.00-0.27%, Range: 0.00-3.60%). The overestimation was correlated positively with the proportion of patients with recrudescence [Pearson's correlation coefficient (ρ): 0.38, 95% Confidence Interval (CI) 0.30-0.46] or new infection [ρ: 0.43; 95% CI 0.35-0.54]. In three study arms, the point estimates of failure were greater than 10% (the WHO threshold for withdrawing antimalarials) when the K-M method was used, but remained below 10% when using the CIF approach, but the 95% confidence interval included this threshold. Conclusions: The 1 minus K-M method resulted in a marginal overestimation of recrudescence that became increasingly pronounced as antimalarial efficacy declined, particularly when the observed proportion of new infection was high. The CIF approach provides an alternative approach for derivation of failure estimates in antimalarial trials, particularly in high transmission settings